CORRELATION OF UAV-BASED MULTISPECTRAL VEGETATION INDICES AND LEAF COLOR CHART OBSERVATIONS FOR NITROGEN CONCENTRATION ASSESSMENT ON RICE CROPS

Abstract. Fertilizer application is a crucial farming operation for regulating crop health thus crop yield. Optimal fertilizing doubles agricultural production subsequently raising farmers’ income, food security and economic agriproducts. To optimize the application of fertilizers, initial monitoring of the current nutrient status of the crops is required. This research will focus on Nitrogen (N), the most extensive fertilizer nutrient in crop cultivation. Conventional N monitoring involves the use of Leaf Color Charts (LCC) wherein leaf color intensity is associated with the N content of the crops. Despite its ability to quantify the optimal amount of needed fertilizers, the LCC method requires extensive on-site labor and lacks accuracy. This study developed a method that incorporates capabilities of Unmanned Aerial Vehicles (UAVs) equipped with a multispectral sensor in N monitoring specifically in rice crops, a major agricultural product in the Philippines. In situ N level information collected through LCC was correlated with remote sensing data, particularly vegetation indices (VIs) extracted from UAV multispectral imagery of a rice plantation in San Rafael, Bulacan. Several VIs sensitive to crop N content were tested to determine which has the highest correlation with the LCC data. Through Pearson correlation and regression analysis, NDVI_{Red Edge} was found to be the most strongly correlated with LCC data suggesting its potential in mapping variability in fertilizer requirements. An equation modelling LCC observations and NDVI_{Red Edge} values that estimates the N levels of an entire rice plantation was generated along with the N concentration map of the study area.